AI in the Courtroom: What High-Stakes AI Governance Really Requires

New Research: Governing AI in High-Stakes Systems — What Enterprises Need to Know

AI is increasingly shaping court filings, evidence, judicial drafting, and public-facing court systems. That raises a critical question for enterprises deploying AI in regulated or high-stakes environments: What does it take to govern AI when trust and legitimacy are non-negotiable?

In a new paper accepted for a symposium of the Cambridge Forum on AI: Law and Governance, Aymara co-founder and CEO Juan Manuel Contreras, Ph.D., and Megan Carpenter, Dean of the University of New Hampshire Franklin Pierce School of Law, to examine what responsible governance looks like when AI touches institutional decision-making.

Adjudication as a Stress Test for AI

Courts operate under strict expectations, much like many enterprise environments. Errors are not just technical failures. They can undermine the Court's operations (due process), accountability, and brand (public trust).

Traditional AI governance approaches were not designed for these risks.

Key Findings at a Glance

1. AI Enters High-Stakes Systems Through Multiple Entry Points

AI does not arrive as a single tool. Filings, evidence, decision support, and public interfaces each introduce distinct governance challenges.

2. Accuracy Is Necessary but Not Sufficient

Even highly capable models can fail institutionally through hallucinations, hidden AI reliance, or unverifiable outputs.

3. Governance Is Not a Checklist, It Is a Cycle

Our research introduces a simple but rigorous governance model for adjudication-grade AI, with direct implications for enterprise AI:

• Education: Domain-specific training for models

• Evaluation: Task-specific testing for hallucinations, bias, and reliability

• Evolution: Continuous monitoring, audits, and institutional learning over time

What This Means for Enterprises

• Regulatory Alignment: AI influencing decisions or records will face increasing scrutiny

• Vendor Due Diligence: Buyers will expect measurable evidence of safety and governance, not claims

• Trust as a Feature: In high-stakes environments, trustworthiness must be designed and measured

Why Aymara

At Aymara, we build tools to evaluate and govern AI where errors matter most, including independent evaluation, benchmarking, and continuous monitoring. Courts are one of the clearest examples of why independent measurement and continuous oversight are essential. The same principles apply across regulated enterprise AI.

Want to understand how your AI systems perform under real-world governance expectations?